For my final project in this course, my partner and I combined our love of travel with data science tools to explore two datasets: one on international tourism trends and another on in-flight etiquette, specifically seat reclining habits. We used public datasets from Gapminder, the World Bank, and FiveThirtyEight, and performed data cleaning, visualization, and modeling using Python (pandas).
We built a K-nearest neighbors model to predict whether a passenger reclines their seat based on features like height and age—ultimately improving its accuracy from 23% to 81%. We also explored an ensemble model to predict CO₂ emissions based on tourism-related variables. While our models revealed limitations, the project strengthened our skills in data wrangling, visualization, and machine learning—while having a lot of fun along the way.
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